Search Results

Abstract Autonomous vehicles (AVs) rely heavily upon their perception subsystems to “see” the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus, it is imperative to test the vehicle extensively in all conditions which it may experience. However, the development of robust AV subsystems requires repeatable, controlled testing—while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real world being developed.

Abstract The use of appropriate loads and regulations is of great importance in weld fatigue assessment of rail on-track maintenance equipment and similar vehicles for optimized design. The regulations and available loads, however, are often generalized for several categories, which proves to be overly conservative for some specific categories of machines. EN (European Norm) and AAR (Association of American Railroads) regulations play a pivotal role in determining the applicable loads and acceptance criteria within this study. The availability of track-induced fatigue load data for the cumulative damage approach in track maintenance machines is often limited. Consequently, the FEA-based validation of rail track maintenance equipment often resorts to the infinite life approach rather than cumulative damage approach for track-induced travel loads, resulting in overly conservative designs.

Abstract This study investigates the behavior of pre-chamber knock in comparison to traditional spark ignition engine knock, using a modified constant-volume gasoline engine with an optically accessible piston. The aim is to provide a deeper understanding of pre-chamber knock combustion and its potential for mitigating knock. Five passive pre-chambers with different nozzle diameters, volumes, and nozzle numbers were tested, and nitrogen dilution was varied from 0% to 10%. The stochastic nature of knock behavior necessitates the use of statistical methods, leading to the proposal of a high-frequency band-pass filter (37–43 kHz) as an alternative pre-chamber knock metric. Pre-chamber knock combustion was found to exhibit fewer strong knock cycles compared to SI engines, indicating its potential for mitigating knock intensity. High-speed images revealed pre-chamber knock primarily occurs near the liner, where end-gas knock is typically exhibited.

Abstract Dynamic stresses exist in parts of a catalyst muffler caused by the vibration of a moving vehicle, and it is important to clarify and predict the vibration response properties for preventing fatigue failures. Assuming a vibration isolating installation in the vehicle frame, the vibration transmissibility and local dynamic stress of the catalyst muffler were examined through a vibration machine. Based on the measured data and by systematically taking vibration theories into consideration, a new prediction method of the vibration modes and parameters was proposed that takes account of vibration isolating and damping. A lumped vibration model with the six-element and one mass point was set up, and the vibration response parameters were analyzed accurately from equations of motion. In the vibration test, resonance peaks from the hanging bracket, rubber bush, and muffler parts were confirmed in three excitation drives, and local stress peaks were coordinate with them as well.

Abstract When the vehicle is under braking condition in the longitudinal motion, the vehicle body will tilt due to the inertial force in motion. A high amplitude will result in uncomfortable feelings of the occupant, such as nervousness or dizziness. To solve the problem, this article presents an adaptive damping system (ADS), which combines the vehicle anti-pitch compensation control with the mixed skyhook (SH) and acceleration-driven-damper (ADD) control algorithm. This ADS can not only improve the vibration effect of the vertical motion for the vehicle but also consider the longitudinal motion of the vehicle body. In addition, a new damper mechanical hardware-in-the-loop test bench is built to verify the effectiveness of the algorithm.

Abstract The crush energy is a key parameter to determine the delta-V in accident reconstructions. Since an accurate car crush profile can be obtained from 3D scanners, this research aims at validating the methods currently used in calculating crush energy from a crush profile. For this validation, a finite element (FE) car model was analyzed using various types of impact conditions to investigate the theory of energy-based accident reconstruction. Two methods exist to calculate the crush energy: the work based on the barrier force and the work based on force calculated by the vehicle acceleration times the vehicle mass. We show that the crush energy calculated from the barrier force was substantially larger than the internal energy calculated from the FE model. Whereas the crush energy calculated from the vehicle acceleration was comparable to the internal energy of the FE model.

Abstract Previous works have established strategies to model artificial test lightning plasma with specific waveform parameters and use the predicted plasma behavior to estimate test specimen damage. To date no computational works have quantified the influence of varying the waveform parameters on the predicted plasma behavior and resulting specimen damage. Herein test standard Waveform B has been modelled and the waveform parameters of “waveform peak,” “rise time,” and “time to reach the post-peak value” have been varied. The plasma and specimen behaviors have been modelled using the Finite Element (FE) method (a Magnetohydrodynamic FE multiphysics model for the plasma, a FE thermal-electric model for the specimen). For the test arrangements modelled herein, it has been found that “peak current” is the key parameter influencing plasma properties and specimen damage.

Abstract Advances in hybrid vehicles and electric vehicles (EV) are creating a need for a new generation of lubricants and new lubricant performance tests. Copper corrosion is one prominent concern for hybrid vehicles and EVs and is routinely assessed using a coupon test. This is characterized as metal dissolution, a surface tarnish, or a corrosion layer where a corrosion product remains on the surface and is characterized by a qualitative visual rating. This deficiency does not provide insight into the nature of the corrosion deposit. In an electric drive unit, there are multiple sources of the electric potential present, which can significantly alter the formation of a corrosion deposit which is not assessed in the coupon tests. The formation of a conductive corrosion deposit can result in catastrophic failure of the electric drive unit, either through direct shorting of the motor winding or failure of the power electronics.

Abstract The automotive industry offers many applications for machine learning (ML), in general, and deep neural networks in particular. However, the real-world deployment of neural networks into safety-critical components remains a challenge as models would need to offer robustness under a wide range of operating conditions. In this work, we focus on uncertainty estimation, which can be used to deliver predictors that fail gracefully, by detecting situations where their predictions are unreliable. Following Gräber et al. [1], we use Recurrent Neural Networks (RNNs) to perform sideslip angle estimation. To perform robust uncertainty estimation, we augment the RNNs with generative models. We demonstrate the advantage of the proposed model architecture over Monte Carlo (MC) dropout [2] on the Revs data set [3].

Abstract Sustainable practices are taking precedence across many industries, as evident from their shift towards the use of environmentally responsible materials, such as natural fiber-reinforced acrylated epoxidized soybean oil (NF-AESO). However, due to the lower reactivity of AESO, the curing reaction usually requires higher temperatures and longer curing time (e.g., 150°C for 6-12 h), thus making the entire process unsustainable. In this study, we demonstrate the potential power of photons towards manufacturing NF-AESO composites in a sustainable manner at room temperature (RT) within 10 min. Two photoinitiators, i.e., the 2,2-dimethoxy phenylacetophenone (DMPA) and 1-hydroxycyclohexyl phenyl ketone (HCPK), were evaluated and compared with the thermal initiator, i.e., tert-butyl perbenzoate (TBPB). Based on the mechanical performance of the AESOs, the photoinitiation system for NF-AESO was optimized.

Abstract Autonomy and connectivity are considered among the most promising technologies to improve safety and mobility and reduce fuel consumption and travel delay in transportation systems. In this paper, we devise an optimal control-based trajectory planning model that can provide safe and efficient trajectories for the subject vehicle while incorporating platoon formation and lane-changing decisions. We embed this trajectory planning model in a simulation framework to quantify its fuel efficiency and travel time reduction benefits for the subject vehicle in a dynamic traffic environment. Specifically, we compare and analyze the statistical performance of different controller designs in which lane changing or platooning may be enabled, under different values of time (VoTs) for travelers.

Abstract The promise and potential for a future of automated vehicles (AVs) remains great, with safety and societal transformations that may rival the original introduction of the automobile. Yet an inability for industry and governments to define what it means for an AV to drive safely has tempered enthusiasm and risks causing a “winter of AV” just like the one that affected Artificial Intelligence technologies decades ago, which is only now being overcome. Towards this end, the Responsibility-Sensitive Safety (RSS) model was introduced as an open and transparent white-box, an interpretable and scalable formal model that defines minimum safety requirements based on reasonable assumptions of others, balancing safety and usefulness for automated driving vehicles.

Abstract Emerging technologies for connected and automated vehicles (CAVs) are rapidly advancing, and there is an incremental adoption of partial automation systems in existing vehicles. Nevertheless, there are still significant barriers before fully or highly automated vehicles can enter mass production and appear on public roads. These are not only associated with the need to ensure their safe and efficient operation but also with cost and delivery time constraints. A key challenge lies in the testing and validation (T&V) requirements of CAVs, which are expected to be significantly higher than those of traditional and partially automated vehicles. Promising methodologies that can be used toward this goal are scenario-based (SBT) and X-in-the-Loop (XiL) testing. At the same time, complex techniques such as co-simulation and mixed-reality simulation could also provide significant benefits.

Abstract This article presents a new machine learning (ML) development lifecycle which will constitute the core of the new aeronautical standard on ML called AS6983, jointly being developed by working group WG-114/G34 of EUROCAE and SAE. The article also presents a survey of several existing standards and guidelines related to ML in aeronautics, automotive, and industrial domains by comparing and contrasting their scope, purpose, and results.

Abstract Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated.

Abstract A unique torque converter test setup was used to measure the torque transmissibility frequency response function of four torque converter clutch dampers using a stepped, multi-sine-tone, excitation technique. The four torque converter clutch dampers were modeled using a lumped parameter technique, and the damper parameters of stiffness, damping, and friction were estimated using a manual, iterative parameter estimation process. The final damper parameters were selected such that the natural frequency and damping ratio of the simulated torque transmissibility frequency response functions were within 10% and 20% error, respectively, of the experimental modal parameters. This target was achieved for all but one of the tested dampers. The damper models include stiffness nonlinearities, and a speed-dependent friction torque due to centrifugal loading of the damper springs.

Abstract Non-pneumatic tires (NPTs) have been widely used due to their advantages of no occurrence of puncture-related problems, no need of air maintenance, low rolling resistance, and improvement of passenger comfort due to its better shock absorption. It has a variety of applications as in earthmovers, planetary rover, stair-climbing vehicles, and the like. Recently, the unique puncture-proof tire system (UPTIS) NPT has been introduced for passenger vehicles segment. The spoke design of NPT-UPTIS has a significant effect on the overall working performance of tire. Optimized tire performance is a crucial factor for consumers and original equipment manufacturers (OEMs). Hence to optimize the spoke design of NPT-UPTIS spoke, the top and bottom curve of spoke profile have been described in the form of analytical equations. A generative design concept has been introduced to create around 50,000 spoke profiles.

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract The article considers the issues of a systemic approach to studying safety levels in transport operations and ways to increase the safety of the operator-machine system in Russian transport. The principal and problematic issues of reducing the risk of injury by preventing traffic accidents and reducing the severity of their impact have not been sufficiently addressed. When performing transport operations, there are often disagreements between the elements of the “Operator, Machine, and Environment” technological system due to the influence of external conditions and parameters of the constantly-changing environment in the workplace. This leads to a sharp increase in the number of failures of system elements, which reduces the level of safety of transport operations.

Abstract To protect ship equipment of river and sea transport, it is suggested to use polymeric protective coatings based on epoxy diane oligomer ED-20, polyethylene polyamine (PEPA) curing agent and filler, which is a departure from industrial production. Thus the purpose of the work is analysis of major dependency of the properties on the content of fillers that allowed to revealed the critical filler content (furnace black) in composites to form a protective coating with the required set of characteristics. The infrared (IR) spectral analysis was used to investigate the presence of bonds on the surface of particles of the PM-75 furnace black, which allows us to assess the degree of cross-linking of the polymer. The influence of the content of dispersed furnace black on the physicomechanical and thermophysical properties and the structure of the protective coating is investigated.